Geographically Weighted Regression Modeling with Adaptive Gaussian Kernel Weighting on GRDP in Indonesia - Frangly Elviano Tangka; Djoni Hatidja; Winsy Ch.D Weku (Sam Ratulangi University, 2024)
The purpose of this study is to determine the variables that affect Gross Regional Domestic Product (GRDP) in Indonesia in 2022 using Geographically Weighted Regression (GWR) with Adaptive Gaussian kernel weighting function. The data used in this study uses secondary data taken from the website of the Central Bureau of Statistics. The variables used are gross regional domestic product of 34 provinces in Indonesia (Y, in billion rupiah), labor force participation rate (X1, in %), foreign investment (X2, in million dollars), open unemployment rate (X3, in %) and human development index (X4, in %). Data were analyzed using GWR with adaptive gaussian kernel weighting function. GRDP in all provinces on the island of Sumatra (11 provinces), DKI Jakarta province, Banten province, and West Kalimantan province are influenced by foreign investment (X2) and human development index (X4). Meanwhile, GRDP in the other 19 provinces is only influenced by foreign investment (X2). GWR model with adaptive gaussian kernel weighting function is formed differently for each province in Indonesia. Keywords: Adaptive gaussian kernel; GWR; gross regional domestic product